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本文來自於站友的發問：
https://ithelp.ithome.com.tw/questions/10206115#=

一年多以前自己也試著寫了二種迴圈方式的解答，最近又再想起這個解答。
之前的迴圈程式，請直接參考以上的連結，本文不再重複。

這次試著不用迴圈的方式再寫一次解答。

原始資料如下：

計算條件如下：

• (GROUP = 'R') & (GROUP = 'A')，RESULT = GOAT * TIME * 0.3
• (GROUP = 'R') & (GROUP = 'B')，RESULT = GOAT * TIME * 0.5
• (GROUP = 'W') & (GROUP = 'A')，RESULT = GOAT * TIME * 0.4
• (GROUP = 'W') & (GROUP = 'B')，RESULT = GOAT * TIME * 0.3

預期最後結果：

這次改寫的程式如下：

import pandas as pd
import numpy as np

df = pd.DataFrame(
    {
      'GROUP':   ['R','W','W','R','R','W'],
      'GOAT':    ['15/10','10/7/11','9','12','8/13','10'],
      'TYPE':    ['A/B','A/B/B','A','B','B/A','B'],
      'TIME': [2,3,2,2,3,2]
    }
)
df

# 先複製一個dfcopy來做運算
dfcopy = df.copy()
dfcopy['GOAT'] = dfcopy['GOAT'].str.split('/')
dfcopy['TYPE'] = dfcopy['TYPE'].str.split('/')
dfcopy

dfcopy = dfcopy.explode(['GOAT', 'TYPE'])
dfcopy = dfcopy.reset_index()
dfcopy

# 確認一下欄位值的類型
dfcopy.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 10 entries, 0 to 9
Data columns (total 5 columns):
Column Non-Null Count Dtype

0 index 10 non-null int64
1 GROUP 10 non-null object
2 GOAT 10 non-null object
3 TYPE 10 non-null object
4 TIME 10 non-null int64
dtypes: int64(2), object(3)
memory usage: 528.0+ bytes

dfcopy.loc[(dfcopy['GROUP'] == 'R') & (dfcopy['TYPE'] == 'A'), 'RESULT'] = dfcopy['GOAT'].astype(int) * dfcopy['TIME'] * 0.3
dfcopy.loc[(dfcopy['GROUP'] == 'R') & (dfcopy['TYPE'] == 'B'), 'RESULT'] = dfcopy['GOAT'].astype(int) * dfcopy['TIME'] * 0.5
dfcopy.loc[(dfcopy['GROUP'] == 'W') & (dfcopy['TYPE'] == 'A'), 'RESULT'] = dfcopy['GOAT'].astype(int) * dfcopy['TIME'] * 0.4
dfcopy.loc[(dfcopy['GROUP'] == 'W') & (dfcopy['TYPE'] == 'B'), 'RESULT'] = dfcopy['GOAT'].astype(int) * dfcopy['TIME'] * 0.3
dfcopy

dfcopy['RESULT'] = dfcopy['RESULT'].replace('', np.nan)

# 將explode後的資料還原成同一個index
df['RESULT'] = (dfcopy.groupby('index', as_index=True)
                  .agg({'RESULT': lambda x: x.dropna().tolist()})
)
df['RESULT']

# 將list轉換成string
df['RESULT'] = ['/'.join(map(str, l)) for l in df['RESULT']]
df